Basic Data Analysis for Time Series with R

  • 4h 34m
  • DeWayne R. Derryberry
  • John Wiley & Sons (US)
  • 2014

Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals. Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, Basic Data Analysis for Time Series with R also features:

  • Real-world examples to provide readers with practical hands-on experience
  • Multiple R software subroutines employed with graphical displays
  • Numerous exercise sets intended to support readers understanding of the core concepts
  • Specific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets

About the Author

DeWayne R. Derryberry, PhD, is Associate Professor in the Department of Mathematics and Statistics at Idaho State University. Dr. Derryberry has published more than a dozen journal articles and his research interests include meta-analysis, discriminant analysis with messy data, time series analysis of the relationship between several cancers, and geographically-weighted regression.

In this Book

  • R Basics
  • Review Of Regression AndMore About R
  • The Modeling Approach Taken in This Book and Some Examples of Typical Serially Correlated Data
  • Some Comments on Assumptions
  • The Autocorrelation Function and AR(1), AR(2) Models
  • The Moving Average Models Ma(1) and Ma(2)
  • Review of Transcendental Functions and Complex Numbers
  • The Power Spectrum And The Periodogram
  • Smoothers, The Bias-Variance Tradeoff, And The Smoothed Periodogram
  • A Regression Model ForPeriodic Data
  • Model Selection and Cross-Validation
  • Chapter12Fitting Fourier Series
  • Adjusting For Ar(1)Correlation In ComplexModels
  • The Backshift Operator, The Impulse Response Function, And General Arma Models
  • The Yule–Walker Equations And The Partial Autocorrelation Function
  • Modeling Philosophy andComplete Examples
  • Wolf'S Sunspot Number Data
  • An Analysis Of Some Prostate And Breast Cancer Data
  • Christopher Tennant/Ben Crosby Watershed Data
  • Vostok Ice Core Data